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from pathlib import Path |
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DIR_OUTPUT_REQUESTS = Path("requested_models") |
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EVAL_REQUESTS_PATH = Path("eval_requests") |
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banner_url = "https://huggingface.co/datasets/reach-vb/random-images/resolve/main/asr_leaderboard.png" |
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BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 300px; max-width: 600px;"> </div>' |
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TITLE = "<html> <head> <style> h1 {text-align: center;} </style> </head> <body> <h1> 🤗 Open Automatic Speech Recognition Leaderboard </b> </body> </html>" |
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INTRODUCTION_TEXT = "📐 The 🤗 Open ASR Leaderboard ranks and evaluates speech recognition models \ |
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on the Hugging Face Hub. \ |
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\nWe report the Average [WER](https://huggingface.co/spaces/evaluate-metric/wer) (⬇️ lower the better) and [RTFx](https://github.com/NVIDIA/DeepLearningExamples/blob/master/Kaldi/SpeechRecognition/README.md#metrics) (⬆️ higher the better). Models are ranked based on their Average WER, from lowest to highest. Check the 📈 Metrics tab to understand how the models are evaluated. \ |
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\nIf you want results for a model that is not listed here, you can submit a request for it to be included ✉️✨. \ |
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\nThe leaderboard currently focuses on English speech recognition, and will be expanded to multilingual evaluation in later versions." |
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CITATION_TEXT = """@misc{open-asr-leaderboard, |
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title = {Open Automatic Speech Recognition Leaderboard}, |
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author = {Srivastav, Vaibhav and Majumdar, Somshubra and Koluguri, Nithin and Moumen, Adel and Gandhi, Sanchit and others}, |
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year = 2023, |
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publisher = {Hugging Face}, |
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howpublished = "\\url{https://huggingface.co/spaces/hf-audio/open_asr_leaderboard}" |
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} |
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""" |
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METRICS_TAB_TEXT = """ |
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Here you will find details about the speech recognition metrics and datasets reported in our leaderboard. |
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## Metrics |
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Models are evaluated jointly using the Word Error Rate (WER) and Inverse Real Time Factor (RTFx) metrics. The WER metric |
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is used to assess the accuracy of a system, and the RTFx the inference speed. Models are ranked in the leaderboard based |
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on their WER, lowest to highest. |
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Crucially, the WER and RTFx values are computed for the same inference run using a single script. The implication of this is two-fold: |
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1. The WER and RTFx values are coupled: for a given WER, one can expect to achieve the corresponding RTFx. This allows the proposer to trade-off lower WER for higher RTFx should they wish. |
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2. The WER and RTFx values are averaged over all audios in the benchmark (in the order of thousands of audios). |
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For details on reproducing the benchmark numbers, refer to the [Open ASR GitHub repository](https://github.com/huggingface/open_asr_leaderboard#evaluate-a-model). |
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### Word Error Rate (WER) |
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Word Error Rate is used to measure the **accuracy** of automatic speech recognition systems. It calculates the percentage |
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of words in the system's output that differ from the reference (correct) transcript. **A lower WER value indicates higher accuracy**. |
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Take the following example: |
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| Reference: | the | cat | sat | on | the | mat | |
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|-------------|-----|-----|---------|-----|-----|-----| |
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| Prediction: | the | cat | **sit** | on | the | | | |
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| Label: | ✅ | ✅ | S | ✅ | ✅ | D | |
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Here, we have: |
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* 1 substitution ("sit" instead of "sat") |
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* 0 insertions |
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* 1 deletion ("mat" is missing) |
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This gives 2 errors in total. To get our word error rate, we divide the total number of errors (substitutions + insertions + deletions) by the total number of words in our |
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reference (N), which for this example is 6: |
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``` |
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WER = (S + I + D) / N = (1 + 0 + 1) / 6 = 0.333 |
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``` |
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Giving a WER of 0.33, or 33%. For a fair comparison, we calculate **zero-shot** (i.e. pre-trained models only) *normalised WER* for all the model checkpoints, meaning punctuation and casing is removed from the references and predictions. You can find the evaluation code on our [Github repository](https://github.com/huggingface/open_asr_leaderboard). To read more about how the WER is computed, refer to the [Audio Transformers Course](https://huggingface.co/learn/audio-course/chapter5/evaluation). |
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### Inverse Real Time Factor (RTFx) |
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Inverse Real Time Factor is a measure of the **latency** of automatic speech recognition systems, i.e. how long it takes an |
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model to process a given amount of speech. It is defined as: |
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``` |
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RTFx = (number of seconds of audio inferred) / (compute time in seconds) |
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``` |
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Therefore, and RTFx of 1 means a system processes speech as fast as it's spoken, while an RTFx of 2 means it takes half the time. |
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Thus, **a higher RTFx value indicates lower latency**. |
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## How to reproduce our results |
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The ASR Leaderboard will be a continued effort to benchmark open source/access speech recognition models where possible. |
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Along with the Leaderboard we're open-sourcing the codebase used for running these evaluations. |
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For more details head over to our repo at: https://github.com/huggingface/open_asr_leaderboard |
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P.S. We'd love to know which other models you'd like us to benchmark next. Contributions are more than welcome! ♥️ |
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## Benchmark datasets |
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Evaluating Speech Recognition systems is a hard problem. We use the multi-dataset benchmarking strategy proposed in the |
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[ESB paper](https://arxiv.org/abs/2210.13352) to obtain robust evaluation scores for each model. |
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ESB is a benchmark for evaluating the performance of a single automatic speech recognition (ASR) system across a broad |
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set of speech datasets. It comprises eight English speech recognition datasets, capturing a broad range of domains, |
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acoustic conditions, speaker styles, and transcription requirements. As such, it gives a better indication of how |
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a model is likely to perform on downstream ASR compared to evaluating it on one dataset alone. |
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The ESB score is calculated as a macro-average of the WER scores across the ESB datasets. The models in the leaderboard |
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are ranked based on their average WER scores, from lowest to highest. |
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| Dataset | Domain | Speaking Style | Train (h) | Dev (h) | Test (h) | Transcriptions | License | |
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|-----------------------------------------------------------------------------------------|-----------------------------|-----------------------|-----------|---------|----------|--------------------|-----------------| |
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| [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) | Audiobook | Narrated | 960 | 11 | 11 | Normalised | CC-BY-4.0 | |
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| [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) | European Parliament | Oratory | 523 | 5 | 5 | Punctuated | CC0 | |
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| [TED-LIUM](https://huggingface.co/datasets/LIUM/tedlium) | TED talks | Oratory | 454 | 2 | 3 | Normalised | CC-BY-NC-ND 3.0 | |
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| [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) | Audiobook, podcast, YouTube | Narrated, spontaneous | 2500 | 12 | 40 | Punctuated | apache-2.0 | |
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| [SPGISpeech](https://huggingface.co/datasets/kensho/spgispeech) | Financial meetings | Oratory, spontaneous | 4900 | 100 | 100 | Punctuated & Cased | User Agreement | |
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| [Earnings-22](https://huggingface.co/datasets/revdotcom/earnings22) | Financial meetings | Oratory, spontaneous | 105 | 5 | 5 | Punctuated & Cased | CC-BY-SA-4.0 | |
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| [AMI](https://huggingface.co/datasets/edinburghcstr/ami) | Meetings | Spontaneous | 78 | 9 | 9 | Punctuated & Cased | CC-BY-4.0 | |
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For more details on the individual datasets and how models are evaluated to give the ESB score, refer to the [ESB paper](https://arxiv.org/abs/2210.13352). |
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""" |
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LEADERBOARD_CSS = """ |
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#leaderboard-table th .header-content { |
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white-space: nowrap; |
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} |
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#whisper-backends-tab th .header-content { |
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white-space: nowrap; |
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} |
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""" |
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